A Species-Based Multi-Objective Genetic Algorithm for Multi-Objective Optimization Problems

被引：0

作者：

Sun Fuquan ^{[1
]}

Wang Hongfeng ^{[1
]}

Lu Fuqiang ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China

来源：

2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2014年

关键词：

multi-objective optimization problem; evolutionary multi-objective optimization; genetic algorithm; species;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, multi-objective optimization problems (MOOPs) have gained a lot of attentions from the community of evolutionary algorithm since many real-world optimization problems would involve multiple objective functions. In this paper, a species-based multi-objective genetic algorithm (SMOGA) that hybridizes a species method, which was initially designed in GA for multi-modal problems, with the algorithm mechanism of NSGA-II, which was one of well-known MOGAs, is proposed for MOOPs. In order to examine the performance of the proposed algorithm, experiments were carried out to investigate the strength and weakness of SMOGA on a series of test MOOPs in comparison with NSGA-II.

引用

页码：5063 / 5066

页数：4

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